# Competitive Coevolution as an Adversarial Approach to Dynamic Optimization

## TL;DR

This paper proposes a novel framework using competitive coevolutionary algorithms to create solution archives offline for efficient dynamic optimization, addressing the challenge of real-time adaptation in changing environments.

## Contribution

It introduces a new approach that reformulates dynamic problems as static set-oriented problems and employs competitive coevolution to build solution archives offline.

## Key findings

- Empirical results demonstrate the effectiveness of the proposed framework.
- The approach shows potential for handling continuous dynamic constrained optimization.
- Reformulating dynamic problems as static set problems is beneficial.

## Abstract

Dynamic optimization, for which the objective functions change over time, has attracted intensive investigations due to the inherent uncertainty associated with many real-world problems. For its robustness with respect to noise, Evolutionary Algorithms (EAs) have been expected to have great potential for dynamic optimization. On the other hand, EAs are also criticized for its high computational complexity, which appears to be contradictory to the core requirement of real-world dynamic optimization, i.e., fast adaptation (typically in terms of wall-clock time) to the environmental change. So far, whether EAs would indeed lead to a truly effective approach for real-world dynamic optimization remain unclear. In this paper, a new framework of employing EAs in the context of dynamic optimization is explored. We suggest that, instead of online evolving (searching) solutions for the ever-changing objective function, EAs are more suitable for acquiring an archive of solutions in an offline way, which could be adopted to construct a system to provide high-quality solutions efficiently in a dynamic environment. To be specific, we first re-formulate dynamic optimization problems as static set-oriented optimization problems. Then, a particular type of EAs, namely competitive coevolution, is employed to search for the archive of solutions in an adversarial way. The general framework is instantiated for continuous dynamic constrained optimization problems, and the empirical results showed the potential of the proposed framework.